Draws a random (sub)sample (with or without replacement).
Usage
resample_uniform(object, ...)
# S4 method for class 'numeric'
resample_uniform(object, n, size = length(object), replace = FALSE, ...)Value
A numeric matrix with n rows and size columns.
See also
Other resampling methods:
bootstrap(),
jackknife(),
resample_multinomial()
Examples
## Uniform distribution
x <- rnorm(20)
resample_uniform(x, n = 10)
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 0.03688103 -1.47855323 0.12524893 -0.04787012 -1.69616120 -2.72527788
#> [2,] 1.06197512 -0.53746204 0.08197536 1.10331728 -0.03352687 0.94398055
#> [3,] -0.34560366 0.03688103 -0.04787012 1.59039312 -0.20107488 0.48966148
#> [4,] 0.08197536 0.03052902 -1.47855323 -0.34560366 1.59039312 -2.72527788
#> [5,] 0.48966148 -0.03352687 1.06197512 0.12524893 -0.04787012 -1.47855323
#> [6,] 0.48966148 1.06197512 0.03688103 1.59039312 -0.53746204 -0.20107488
#> [7,] -0.34560366 0.03688103 0.94398055 -0.11695822 -0.03352687 -0.53746204
#> [8,] -0.53746204 1.06197512 1.10331728 0.03052902 0.03688103 -0.04787012
#> [9,] 0.08197536 -0.53746204 0.94398055 1.59039312 0.79560748 -2.72527788
#> [10,] -0.34560366 0.03052902 -0.53746204 -0.04787012 0.79560748 1.10331728
#> [,7] [,8] [,9] [,10] [,11] [,12]
#> [1,] 0.48966148 -0.11695822 -0.79489264 -0.20107488 -0.5374620 -0.03352687
#> [2,] 0.48966148 1.59039312 0.03688103 -1.69616120 -0.2010749 -2.72527788
#> [3,] -2.72527788 0.79560748 0.08197536 -0.53746204 0.1252489 -1.69616120
#> [4,] 1.06197512 -0.79489264 0.03688103 0.94398055 0.4896615 0.12524893
#> [5,] 0.03052902 -0.11695822 1.10331728 -1.69616120 1.5903931 0.94398055
#> [6,] -0.04787012 0.79560748 -1.69616120 -0.34560366 -0.7948926 0.03052902
#> [7,] 0.48966148 0.08197536 1.10331728 -2.72527788 0.7956075 1.59039312
#> [8,] 0.12524893 -0.11695822 -0.34560366 -2.72527788 0.9439805 -0.79489264
#> [9,] -1.47855323 0.48966148 -1.69616120 -0.04787012 -0.7948926 -0.03352687
#> [10,] -0.20107488 0.03688103 -1.47855323 -0.79489264 1.5903931 -0.11695822
#> [,13] [,14] [,15] [,16] [,17] [,18]
#> [1,] 1.06197512 0.94398055 -0.34560366 0.03052902 0.79560748 1.5903931
#> [2,] 0.79560748 -0.79489264 -0.04787012 0.12524893 0.03052902 -0.1169582
#> [3,] -0.03352687 -1.47855323 1.10331728 0.94398055 1.06197512 -0.1169582
#> [4,] -0.11695822 -0.20107488 1.10331728 -0.03352687 0.79560748 -0.5374620
#> [5,] -2.72527788 -0.34560366 -0.20107488 0.79560748 -0.79489264 -0.5374620
#> [6,] -0.03352687 -1.47855323 0.94398055 1.10331728 0.12524893 -2.7252779
#> [7,] -0.04787012 -0.20107488 -0.79489264 -1.47855323 0.12524893 1.0619751
#> [8,] 0.08197536 0.48966148 -1.47855323 1.59039312 0.79560748 -0.2010749
#> [9,] -0.20107488 0.03688103 1.06197512 -0.11695822 0.12524893 -0.3456037
#> [10,] 0.94398055 1.06197512 0.48966148 -0.03352687 -1.69616120 0.1252489
#> [,19] [,20]
#> [1,] 1.10331728 0.08197536
#> [2,] -0.34560366 -1.47855323
#> [3,] -0.79489264 0.03052902
#> [4,] -0.04787012 -1.69616120
#> [5,] 0.08197536 0.03688103
#> [6,] 0.08197536 -0.11695822
#> [7,] -1.69616120 0.03052902
#> [8,] -0.03352687 -1.69616120
#> [9,] 1.10331728 0.03052902
#> [10,] 0.08197536 -2.72527788
## Multinomial distribution
x <- sample(1:100, 20, TRUE)
resample_multinomial(x, n = 10)
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
#> [1,] 60 96 90 86 41 65 2 95 98 72 62 75 93
#> [2,] 45 80 105 79 50 81 5 80 97 70 66 72 99
#> [3,] 48 78 99 76 59 94 0 87 93 51 56 76 109
#> [4,] 53 68 91 105 51 65 4 106 92 66 55 71 94
#> [5,] 48 87 103 78 43 69 7 100 99 64 60 74 94
#> [6,] 51 81 91 80 55 89 4 87 82 58 66 78 89
#> [7,] 49 69 97 78 43 94 5 101 96 62 74 78 94
#> [8,] 47 86 103 83 46 74 6 100 106 68 60 69 82
#> [9,] 61 80 97 94 49 79 3 91 82 50 71 72 97
#> [10,] 54 81 101 81 37 87 2 102 95 72 68 71 88
#> [,14] [,15] [,16] [,17] [,18] [,19] [,20]
#> [1,] 104 22 60 52 112 44 20
#> [2,] 104 21 55 51 101 58 30
#> [3,] 112 18 73 32 93 68 27
#> [4,] 115 18 71 52 93 56 23
#> [5,] 116 20 77 46 93 51 20
#> [6,] 116 19 66 56 96 56 29
#> [7,] 108 17 74 52 91 47 20
#> [8,] 86 21 76 42 94 74 26
#> [9,] 103 30 64 51 92 52 31
#> [10,] 103 23 80 35 92 47 30